The present invention relates generally to glass break detectors, and more particularly to glass break detectors that use neural networks to determine if an atmospheric wave was created by glass breaking.
In an era of high crime, property owners are concerned about the security of both their property, and persons on the premises. Thus, security systems for both the home and business have experienced an increase in demand. Glass break detectors are a necessity for any security system that is used to safeguard structures having glass windows and doors.
One approach that has considered involves the use of a window mounted piezoelectric glass detector. This device detects vibrations that occur when glass breaks. These devices have been fairly reliable in detecting glass breaks and do not respond to false conditions such as knocking on the glass. However, piezoelectric glass break detectors have a significant drawback. Multipane windows, or rooms with multiple windows, require separate detectors and wiring for each pane of glass. Thus, these devices can be both costly and unsightly under these circumstances.
In another approach that has been considered, analog audio discriminators have been developed to solve the problems associated with the window mounted units. These units include a microphone for converting the sound to an electrical signal. An analog processor detects glass breakage by measuring the slope of the electrical signal. However, these analog devices also have a significant drawback. They are prone to false alarms because they are unable to discriminate between glass break sounds and similar sounds.
In yet another approach that has been considered, a digital audio discriminator has been developed to reduce the number of false alarms and increase the accuracy of the detector. A Harris 800 supercomputer was used to analyze Power Spectral Density (PSD) of glass break signals and in doing so, identify the dominant features of breaking glass. A neural network was developed and trained to recognize the dominant features identified by the super computer. The resultant digital glass break detector was very sophisticated. It included a microphone that converted sound energy into an electrical signal, analog to digital conversion, and a digital signal processor (DSP) to process the resultant digital signal. The DSP calculated the PSD of the digital signal and extracted the dominant features of the PSD. The dominant features were used by a neural network embedded in the DSP to evaluate the digital signal. There is a significant drawback to this approach as well. The processing power of a DSP is needed to extract the dominant features from the signal and use them to perform neural network processing. Unfortunately, DSPs are expensive and the resultant device may not be affordable for many home owners.
Thus, a need exists for a glass break detector that incorporates the sophistication and accuracy of a neural network without the costs associated with complex digital signal processing.
Existing problems with current state of the art glass break detectors are addressed by the present invention. An inexpensive processor calculates a set of signal characteristics such as the discrete fourier transform coefficients of an acquired time domain signal using a Discrete Fourier Transform or a Fast Fourier Transform. The magnitudes of each coefficient may also be used. A two-layer neural network uses the set of signal characteristics to accurately detect breaking glass. This design eliminates the unnecessary and overly complex processing used in calculating the PSD and evaluating the dominant features of the PSD. Thus, the efficient design of the present invention has no need for an expensive DSP. The present invention is implemented using a floating point processor that costs a few dollars. The present invention features an extremely accurate and low-cost glass break detector.
One aspect of the present invention is an apparatus for detecting breaking glass in an environment. The apparatus comprises: a sensor unit for acquiring a time domain signal from the environment; a characteristic extraction unit connected to the sensor for extracting a set of signal characteristics from the time domain signal; and a classifier connected to the characteristic extraction unit, wherein the set of signal characteristics are used by the classifier as an input data set to determine whether the time domain signal represents breaking glass.
In another aspect, the present invention includes a method for detecting breaking glass in an environment. The method comprising the steps of: acquiring a time domain signal from the environment; extracting a set of signal characteristics from the time domain signal; and, classifying the time domain signal by using the set of signal characteristics as a set of input data. to determine whether the time domain signal represents breaking glass.
In yet another aspect, the present invention includes a method for fabricating an apparatus for detecting breaking glass in an environment. The method comprising the steps of: providing a sensor unit for acquiring a time domain signal from the environment; providing a characteristic extraction unit connected to the sensor for extracting a set of signal characteristics from the time domain signal; providing a classifier connected to the feature extraction element; and training the classifier by inputting a plurality of collected signal samples to the classifier and setting an output of the classifier to a desired value, wherein the classifier is trained to determine whether the time domain signal represents breaking glass by learning to associate the plurality of collected signal samples with a corresponding desired output.
Additional features and advantages of the invention will be set forth in the detailed description which follows, and in part will be readily apparent to those skilled in the art from that description or recognized by practicing the invention as described herein, including the detailed description which follows, the claims, as well as the appended drawings.
It is to be understood that both the foregoing general description and the following detailed description are merely exemplary of the invention, and are intended to provide an overview or framework for understanding the nature and character of the invention as it is claimed. The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. The drawings illustrate various embodiments of the invention, and together with the description serve to explain the principles and operation of the invention.